Emotion Recognition Based On Eeg Signal

Emotions play a vital role in human communication. Emotions can be conveyed in different ways verbally, facial recognition and gestures and by different tones. Different methods like facial recognition and speech enhancement have been used for emotion analysis which also showed pretty good results but also had major drawbacks. These techniques would require the subject to show expression for the different emotions they are going through which is possible for a normal subject but may not be possible for disabled individual. EEG is one of the non-invasive technique which is very easy to record and do not need any expressions which makes it advantageous than other methods. Cortical activity of brain can be easily recorded from a disable person and it gives the exact measure of the emotion state a person is going through with fewer artefacts. Different techniques have been used to process the EEG signal for emotion recognition with various results. Wavelet transform is one of the best methods used to decompose the sub bands affectively. Bionic wavelet transform is a new time frequency analysis method recently proposed (Yao 2001). It defers from Wavelet transform by a parameter called T-function. In this work we have taken data from enterface06_emobrain database where 5 subjects were used. While there electrical activity was recorded they were prompted by different images using International Affective Picture System. They were divided in three categories: sad, neutral and happy. All the subjects went through these three sessions and their emotions were recorded. We used bionic wavelet transform to classify these signals which can be used for diagnostic purposes. The classification accuracy was 89% best case scenario which is better than wavelet transform which showed 64% accuracy.

1 INTRODUCTION

Emotions play a very important role in a human's life. Emotions are an essential part of humans thought and action. It plays an important role in human brain perception, reasoning [1] and conclusion. Recognition of emotions by physiological means is a matter of interest for both psychologists and engineers [2]. Human depends on their emotional state and they take decisions according to it. In brain research and reasoning, feeling is a subjective, cognizant experience portrayed fundamentally by psychophysiological outflows, organic responses, and mental states. Feeling is frequently co-partnered and acknowledged proportionally compelling with inclination, demeanor, identity, mien, and motivation. It additionally is affected by hormones and neurotransmitters. Emotion [1, 2 and 4] is regularly the main impetus behind inspiration, positive or negative. An elective meaning of feeling is a "positive or negative encounter that is connected with a specific example of physiological movement."
The physiology of feeling is nearly connected to arousal of the sensory system with different states and qualities of arousal relating, clearly, to specific feelings. Feelings are a complex state of feeling that brings about physical and mental changes that impact our conduct. Those acting principally on feeling may appear to be as though they are not considering, yet comprehension is a critical part of feeling, especially the translation of occasions. For instance, the knowledge of trepidation typically happens because of a risk. The cognizance of peril and consequent arousal of the sensory system is a fundamental part to the ensuing translation and marking of that arousal as an enthusiastic state. Feeling is likewise joined to behavioral propensity. Extroverted individuals are less averse to be social and express their feelings, while independent individuals are more inclined to be all the more socially withdrawn and disguise their emotions.

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Figure 1 1: A 2D representation of emotion
Human brain is a complex system and has rich spatiotemporal [2] dynamics. It is the basis of intellect, translator of the logic, originator of movement and controller of behavior. The electrical signals produced by the body, together with chemical reactions allow the other part of the body to communicate. There are lots of non-invasive techniques available for human brain dynamics like facial recognition [2], speech evaluation. These techniques provide very good results but also have major downsides where the subject needs to express their feelings which can be difficult for a subject not healthy.
1.1 HISTORY
The statement "EMOTION' goes again to 1579, when it was adjusted from the French word ??mouvoir, which signifies "to blend up". In any case, the most punctual antecedents of the statement likely go over to the precise sources of dialect. Feelings have been portrayed as discrete and steady reactions to inner or outside occasions which have a specific essentialness for the living being. Feelings are concise in term and comprise of a facilitated set of reactions, which may incorporate verbal, physiological, behavioral, and neural systems. Feelings have additionally been depicted as organically given and a consequence of development in light of the fact that they gave great answers for aged and repeating issues that confronted our progenitors.
In Scherer's segments transforming model of emotion [8], five essential components of feeling are said to exist. From the segment preparing point of view, feeling knowledge is said to oblige that these procedures get composed and synchronized for a brief time of time, determined by evaluation forms. In spite of the fact that the consideration of cognitive examination as one of the components is somewhat disputable, since a few scholars make the presumption that feeling and comprehension are particular yet cooperating frameworks, the part preparing model gives a succession of occasions that viably portrays the coordination included throughout a passionate scene.
' Cognitive evaluation: gives an assessment of occasions and items.
' Substantial side effects: the physiological part of passionate experience.
' Movement propensities: a motivational part for the arrangement and bearing of engine reactions.
' Interpretation: facial and vocal representation very nearly dependably goes with a passionate state to convey response and proposition of movements.
' Sentiments: the subjective knowledge of passionate state once it has happened.

1.2 EMOTION CLASSIFICATION IS CHALLENGING
Measuring "emotion" [2, 4 and 6] is testing on the grounds that individuals have their unique methods for imparting and there are numerous variables that influence their passionate state past the current discussion. We would say, a successful measure of unsettling might be made by catching changes in the anxiety levels and discourse rhythm of the discussion. More elevated amounts of progress in anxiety and rhythm are regularly connected with a larger amount of fomentation.

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Figure 1 2: Facial Expression
This measure of fomentation is progressively genuine when it is seen over a huge collection of calls. By taking a gander at a bigger number of gets the irregular variables smooth out and the tumult measure gets more helpful. Case in point, in a full week's value of calls, the normal tumult on the calls took care of by specific executors, or about specific points, will be reliably higher than different operators or themes. This investigation can give profitable confirmation about what components are influencing the client experience.
1.3 ELECTROENCEPHALOGRAPHY
Electroencephalography (EEG) gives a direct measure of the cortical activity [2]. The measurement using EEG has helped researchers to work on a large variety of areas like motor imagery, BCI, emotion analysis. It is a non-invasive technique which helps in both neuroscience research and diagnosis technique by using electrodes on scalp and recording the electrical activity. In emotion analysis EEG [4, 5, 7 and 8] is advantageous as the recording can be directly taken from scalp and EEG [1, 8, 9 and 10] can give more accurate measure and the subject does not need to express feelings and hence it can be used for diagnostic purposes for disabled ones.

Inquire about on emotion [12, 14, 16 and 17] has expanded altogether in the course of recent decades with numerous fields helping including brain science, neuroscience, endocrinology, solution, history, humanism, and much software engineering. The various speculations that endeavor to clarify the inception, neurobiology, encounter, and capacity of feelings have just cultivated more compelling research on this point. Momentum regions of exploration in the idea of feeling incorporate the advancement of materials that empower and inspire feeling. In recent years, researchers have used many methods and algorithms to classify the emotions and each method has given their results. Methods have been improved to get better results in these years. These techniques yielding better results can be applied for diagnostic purposes like depression, psychiatric problems, etc.
1.4 PROBLEM DEFINITION
This is aggravated by the way that speakers frequently express mixed feelings, for example, both sympathy and disturbance which are massively troublesome to order. Additionally, conclusion dissection is frequently unequipped for altering for the changed ways diverse guests express the same feelings, for instance, individuals from the North East or Scotland may be brusquer while guests from the South West have a tendency to be more well-mannered actually when disappointed. These constraints highlight its non-feasibility as a business investigation apparatus.
Before starting with the work, it is better to define exactly the aim of this work. In my work I want to discover a better algorithm to classify emotions so that it can be used more accurately for different purposes. Specifically, three emotions were considered for this work i.e. sad, neutral and happy. I want to develop a more accurate method to classify these emotions. The data for this work is taken from enterface06_emobrain database. Five subjects were showed pictures from International Affective Picture System to see the how they react when they are watching these pictures. These subjects reacted differently for different emotions and also that out of the five subjects two subject showed completely opposite emotion for when they were shown picture for a particular emotion.

1.5 THESIS ROADMAP
The thesis is structured as follows:
' Chapter 2 gives the background for the work which has already been done by various researchers in emotions detection. It also gives the theories behind emotion recognition and also gives various information used behind emotion analysis.
' Chapter 3 gives the definition of the methods and the mathematical derivation of the methods used before and the method to be used for this work. It defines all the parameters we need for the assessment of the signals acquired. It also explains the methodology and the parameters as to why it has been modified and the advantage behind it.
' Chapter 4 explains how the definitions of the above parameter have been used in the algorithm. It also gives a thorough detail about how the methodology is done and what are the problems faced.
' Chapter 5 displays the results of the methods used in the form of graphs and charts. Explanation of the results has been also made there.
' Chapter 6 provides conclusion of the results and pr??cises the work provided in this thesis.

2 BACKGROUND AND WORK DONE
This chapter defines various theories in emotion [2] and it also gives the literature review of various works done before on emotion analysis [2]. We narrow down our review to classifying three emotions. We see that how beneficial their work has been to the world of science with the results the acquired.
2.1 THEORIES
Emotions have pulled in specialists for quite a while now as should be obvious the different work done on emotion examination. There are different methods like facial declaration [1], speech recognition [2], emotion through content [3] and movements to perceive diverse emotions. Analysts from different fields have had the capacity to chip away at feeling dissection utilizing these wide extends of routes accessible to get the information for feeling investigation.
'The French philosopher Ren?? Descartes' treatise, Les passions de l'??me (Passions of the
Soul), published in 1649, is considered to be among the earliest works to theorize emotions (Anscombe and Geach, 1970; Cowie, 2000)' [3]. The initial theory only classified two emotions primary [3] emotion and secondary [3] emotion.
In the last decade, researchers have explored several parts of human emotion so that they could conclude to a set of emotion categories that are universally acceptable (Picard, 1997) [17]. Numerous works in this area have been reported in the literature (Tomkins, 1962; Izard, 1977; Plutchik, 1980; Ortony et. al., 1988; and Ekman, 1992).
The vector model of feeling initially showed up in 1992 [3]. This two-dimensional model comprises of vectors that point in two headings, speaking to a "boomerang" shape. The model accept that there is dependably an underlying arousal measurement, and that valence decides the course in which a specific feeling untruths. For instance, a positive valence might move the feeling up the top vector and a negative valence might move the feeling down the lowest part vector. In this model, high arousal states are separated by their valence, although low arousal states are more nonpartisan and are spoken to close to the gathering purpose of the vectors. Vector models have been most broadly utilized within the testing of word and picture stimuli [3].
Robert Plutchik offers a three-dimensional model that is a cross breed of both essential-complex classes and dimensional hypotheses. It organizes feelings in concentric loops where inward rounds are more essential and external rings more unpredictable. Strikingly, external rings are additionally structured by mixing the internal loop feelings. Plutchik's model, as Russell's, exudes from a circumplex representation, where enthusiastic words were plotted focused around similarity [15 and 24]. In software engineering, Plutchik's model is frequently utilized, in distinctive structures or versions,[16] for errands, for example, full of feeling human-machine association or assumption dissection. Robert Plutchik made another origination of feelings in 1980. He called it the "wheel of feelings" in light of the fact that it showed how distinctive feelings can mix into each other and make new feelings. Plutchik initially proposed 8 essential bipolar feelings: happiness versus bitterness; outrage versus alarm; trust versus disturb; and shock versus suspicion. From that point Plutchik recognized more exceptional feelings focused around their disparities in intensities. In the event that you take a gander at the chart underneath you can perceive how every feeling identifies with the other:

Figure 2 1:Plutchik's model of Emotion
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Ekman contrived his rundown of essential feelings in the wake of doing examination on numerous diverse societies. He might depict a circumstance and ask people to pick a facial declaration that best fit. He might likewise show photos of diverse facial statements and ask people to recognize the feeling. Over all societies concentrated on, Ekman discovered 6 fundamental feelings:
' Anger
' Disgust
' Fear
' Happiness
' Sadness
' Surprise
Ekman added to this list in the 1990s, but stated that not all of these can be encoded via facial expressions:
' Amusement
' Contempt
' Contentment
' Embarrassment
' Excitement
' Guilt
' Pride in achievement
' Relief
' Satisfaction
' Sensory pleasure
' Shame


Table 2 1:Different emotions discovered
The most nuanced grouping of feelings so far is likely Parrots' 2001 hypothesis. Parrot distinguished in excess of 100+ feelings and conceptualized them as a tree organized rundown:

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Figure 2 2:Parot's classification emotion-1

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Figure 2 3:Parot's classification of emotion-2
Examination of facial representation of feeling is carried out to characterize the essential articulations and contrast them with the fundamental feelings. Ekman (1992) [3] characterized fundamental feelings relying upon the facial articulation and it was overall acknowledged by distinctive researchers. These feeling arranged have likewise been utilized as a part of other computational methodologies to feeling distinguishment (Liu et al.,2003; Alm et al., 2005; and Neviarouskaya et al., 2007a,b) [3].
The circumplex hypothesis of Affect (Watson and Tellegen,1985) shows two fundamental part of positive and negative influence, which run from high to low. It likewise indicates an alternate set of viewpoints in the model, which holds charm- offensiveness and engagement-withdrawal. These diverse viewpoints are indicated in the model every arrangement where every grouping is portrayed in few words. Depictions were made on evaluation toward oneself of subjects (Rubin et al., 2004) [3].
A few specialists, for example, Schlosberg (1954) [3] have alluded to persistent measurements of feeling rather than unique feeling classifications. There is assertion around analysts on at slightest two of these measurements: valence (positive/negative) and arousal (quiet/energized) (Barrett, 1998) [3]. Plutchik (1980) [3] and Frida et al. (1992) [3] have highlighted the part of the force segment in the investigation of feeling.

Figure 2 4: Different emotion and there description
A few works have been accounted for in the writing on the investigation of feeling articulation in writings. The open capacity model of dialect presented by the Russian-American etymologist, Roman Jacobson (1960) [3] recognizes emotive capacity as one the six capacities of dialect. The composed articulation of feeling needs signals, tones, and facial representations, furthermore rather depends on innovative utilization of words for conveying feeling. Johnson-Laird and Oatley (1989) [3] have found essential feelings by dissecting 590 English words, which depict feeling. Osgood's hypothesis of Semantic Differentiation (Osgood et al., 1957) [3] arrangements with appointing emotive implications to words along three measurements. Osgood et al. performed variable examination of writings to distinguish three fundamental elements on which the emotional words might be appraised. The three components are: evaluative element (great or awful), intensity variable (solid or feeble), and movement component (dynamic or uninvolved). The evaluative variable conveys the strongest relative weight, furthermore a few works have concentrated on it (Kamps and Marx, 2002; Turney and Littman, 2003; furthermore Mullen and Collier, 2004) [3].
A few words pass on feeling expressly, while some different words could be utilized to pass on feeling certainly relying upon the setting (Clore et al., 1987) [3]. Strapparava and Valitutti (2006) [3] have ordered words into 'immediate full of feeling words' (express) and 'aberrant emotional words' (implied) classes. My work has used both these sorts of words. The tests reported in this postulation demonstrate that it is critical to consider a mixture of feeling-related words for programmed distinguishment of feeling, including immediate and circuitous emotional words.
2.2 EMOTION ANALYSIS
In 2005 Andrew J, et al., worked on alpha band recognition for depression. The reason for this paper is to apply another screening procedure to the recordings that we estimate will show which EEG follow are liable to give all the more indicatively dependable FBA degrees. Past work from this lab included making a setup for FBA recordings as a major aspect of an alternate study. Throughout visual investigation of EEG spectra for programming approval purposes, it was recognized that not all the subjects had overall characterized alpha crests. Developing this, we recommend the alpha area of the frontal EEG signal [21, 22, 25 and 27] has 2 parts, one identifying with the alpha-dormancy instrument simply portrayed, the other comprising of visual antiquities (in spite of the fact that a large number of these might be evacuated) or neural commotion from different sources not connected with the AIM [10 and 32]. The AIM [10 and 32] Alpha related waves have a tendency to have a honestly limited transfer speed, so appearance of a reasonable top in the 8-13 Hz district ought to demonstrate their vicinity. In subjects without an agreeable alpha top, we suggest that frontal asymmetry computations might be focused around to a great extent non - AIM commotion, and along these lines be problematic.
Recording terminals were joined, and the subjects were taught to rests and to diminish development antiques. Each one recording was 5 minutes in length of time, with the subjects' eyes shut all around the recording time. Recordings were handled logged off to acquire Fp1 (left prefrontal cortex) and Fp2 (right prefrontal cortex) computationally re-referenced to arrive at the midpoint of ear cartilage, for reasons given in [10]. An additional 2 channels were utilized to record the vertical and level Electrooculogram (EOG) for later visual antique removal. A GUI programming bundle was produced in MATLAB 6.5 [10] to process the data. The spectra of every non-dismisses 2-second window were found the middle value of together. To be considered having an 'acceptable alpha crest' [10], an arrived at the midpoint of spectra follow needed to pass a set of rules [10]. By the 'agreeable alpha crest' governs, 19 subjects were grouped as having an alpha top, and the staying 6 with no alpha top. Subjects presently bringing energizer medicine or with bipolar despondency are shown [10].
Human thought is intrinsically passionate and feelings are a crucial and beneficial part of human thought and activity. Feelings however expected to be inconsistent from the good 'ol days, are ended up being a component that includes preparation to act, desires, center to objectives, examination ' of self as well as other people and the ensuing responses. Perception without anyone else present means the discernment, experience and representation of feelings. While encountering the feeling, there are additionally physiological changes [11] occurring in the human body, in the same way as varieties in the heart rate (ecg/HRV) [11], skin conductance (gsr) [11], breathing rate (br) [11], blood volume pulse (bvp) [11], brain waves (EEG) [11], temperature and muscle strain, and these are a percentage of the measurements to sense emotive coefficient. The subjects encountering feelings in a higher extent contrast from the individuals who can manage these enthusiastic encounters and such a component is named as Emotional Discernment [11]. This distinction in experience is expected a triad ' esteem, desire and actuality; which structures an enthusiastic self-structure. Despite the fact that there is dependably an enthusiastic encounter in some extent, the outflow may be obvious or totally covered or the subject is misleading or tries to conceal the feeling by not communicating. This paper takes a gander at the courses in which the boosts for activating off the passionate state of the subject are recognized, connected and henceforth seen by the subject(s) and parameters like GSR and BVP or PR [11] and their varieties uncovered to close the enthusiastic state of the subject. The subject additionally gives his input about the emotions he is experiencing and consequently encourages acceptance.

The subjects need to put the left and right fingers, one each on each of the silver terminals. The GSR quality is showed on the LCD screen. At first, the subjects' GSR, under typical conditions is measured, that serves as the reference for us to gauge the fluctuation of the qualities, in diverse passionate states. The standard is chosen based upon the way that a subject takes limited time to get excited from ordinary state, called the standard. What's more once the state is arrived at, he/she has a tendency to be in that state for a limited sum of time or as long as the jolt is connected. The aggregate time of jolt for every feeling is in the middle of "2-3" minutes [11], with a crevice of "< = 1minute" [11] between distinctive boosts, throughout which the music is put off and the subject is prompted to come to ordinary, have a taste of water, crunch on a scone and so forth.
Closing, GSR [11] gauges the passionate state of individual, knowing which, responses to the same circumstance, by distinctive individuals could be caught on. The levels of arousal to the same boosts might let an analyst judge the enthusiastic regulation competence of people, which actually, decides their mental wellbeing. Feeling regulation capacity is termed by cognitive researchers as Emotional Brainpower, providing for us another metric for IQ [11] estimation. The work thinks that it provision in the spaces of guard for faculty recruitment, being a test for anxiety administration.
Rendi E. J. Yohanes used DWT to classify emotions. In his paper, he proposed to utilize DWT coefficients as characteristics for feeling distinguishment from EEG [2][11] signals. Past characteristic extraction routines utilized force spectra thickness values derived from Fourier Transform or sub-band vitality and entropy determined from Wavelet Transform. These characteristic extraction systems wipe out fleeting data which are key for examining EEG indicators. The DWT coefficients speak to the level of association between the examined indicator furthermore the wavelet capacity at diverse occurrences of time; in this way, DWT coefficients hold fleeting data of the broke down indicator. The proposed characteristic extraction system completely uses the concurrent time-recurrence dissection of DWT by safeguarding the fleeting data in the DWT coefficients. In this paper, we additionally concentrate on the impacts of utilizing distinctive wavelet capacities (Coiflets, Daubechies and Symlets) [12] on the execution of the feeling distinguishment framework. The info EEG indicators were gotten from two terminals as indicated by 10-20 frameworks: 'Fp1 and Fp2' [12]. Visual boosts from International Affective Picture System (IAPS) [6, 7, and 11] were utilized to affect two feelings: joyful and pitiful. Two classifiers were utilized: Extreme Learning Machine (ELM) [12] and Support Vector Machine (SVM) [12]. Exploratory effects affirmed that the proposed DWT [12] coefficients system demonstrated change of execution contrasted with past routines.
In this paper, he likewise considers the impact of utilizing distinctive wavelet works on the execution of the feeling distinguishment framework. The waveforms of the wavelet capacity should be as like the transient action to be identified in the EEG indicators. Be that as it may, since the ideal waveform for is obscure, different sorts of wavelet capacity were acknowledged: Daubechies ('db4') Order 1 to 20, Symlets ('sym') Order 1 to 20 and Coiflets ('coif') request 1 to 5. The execution of the distinctive sorts of wavelet capacities demonstrated their suitability to distinguish transient movement in EEG signals, which compare to either euphoric or tragic emotion.
Test effects indicated that PCA enhanced the arrangement correctness of the framework all in all. The characteristics inferred from the EEG sign utilizing DWT held other data which may not be feeling related. These data might not shift significantly between pitiful and euphoric EEG indicators, in this way applying PCA might uproot such data. There were four wavelet capacities that indicated the best exhibitions: Symlets [12] request 6 and 19, Daubechies [6] request 4 also Coiflets request 1. Exploratory effects indicated vast variety in feeling order execution for other sorts of wavelet capacities. These outcomes demonstrate the basic significance of picking the right sort of wavelet capacity for feeling distinguishment [12] from EEG indicators. Symlets [12] request 6 was picked as the best wavelet work as it for the most part demonstrated preferable execution over other wavelet capacities. This may demonstrate that the waveform of Symlets [12] request 6 is like the transient exercises in the EEG indicates that compare to pitiful or cheerful feelings.
Teodiano Freire Bastos-Filho [13] in 2012 presented a paper in which investigation of three EEG indicators characteristic extraction procedures were done. These procedures have been generally utilized in scrutinizes of enthusiastic states distinguishment: measurable aspects, characteristics focused around PSD (Power Spectral Density) [13] and characteristics focused around HOC (High Order Crossings) [13]. The approval was performed through grouping of passionate states of smooth and anxiety utilizing the K-NN [13] based classifier in off-linemode utilizing EEG signs from accessible DEAP database. The best comes about accomplished were 70.1% [13], utilizing the PSD based procedure, and 69.59% [13] utilizing the HOC [13] based procedure.
The DEAP database gives, notwithstanding the crude information, preprocessed information which have been sub inspected at 128hz, sifted with a bandpass channel from 4.0 to 45.0 Hz, found the middle value of with the basic reference utilizing MATLAB [13] furthermore had the electrooculogram information evacuated [13]. The characteristics extraction systems assessed utilizing the preprocessed indicators are depicted beneath. In every one of the three cases, in the wake of computing the characteristic vector, every component of the vector is standardized after calculating the feature vector, each element of the vector is normalized. The Power Spectral Density (PSD) is around the strategies connected in the characterization of examples of mind action display in EEG signs and is as of now being utilized within investigations of feeling distinguishment [13]. As the EEG rhythms are characterized basically in the recurrence space, what's more dissecting the EEG indicator, PSD [13] communicates the level of action in each one band of signals that permits the parts of force to be specifically deciphered as mind rhythms. Recognizing a Fourier-based methodology, the PSD [13] is evaluated here by the technique for Welch. In the event that thinking about these effects one can see that the systems in view of PSD [13] and HOC [13] accomplished better comes about than those focused around characteristics facts of time area signal [13].
CHEN Dongwei in 2013 presented a model of wearable full of emotion [1] figuring was proposed for segregating diverse enthusiastic states and developing the Internet of Brains, by method for powerful connectivity of EEG-based cerebrum system. Firstly, we proposed a sound feeling-incited mental trial to gather the EEG information under distinctive enthusiastic states. At that point, Independent Component Analysis (ICA) [1] was utilized to deteriorate distinctive free parts focused around distinctive enthusiastic states; Granger Causality Analysis (GCA) [1] was used to distinguish the intuitive conditions between every autonomous segment in place to develop the causal connectivity mind system. Dynamic qualities, including causal thickness and causal stream, were concentrated focused around Graph Theory. At long last, the comparing law between attributes of EEG example and "internal" enthusiastic state was ran across to build full of feeling figuring model. Moreover, the model of wearable emotional processing was developed focused around above law with the compact EEG procurement gadget, and model arrangement of wearable full of feeling registering focused around web of Brains was accomplished for BCI [1]. We executed ICA [1] with Fastica calculation to restrict the diverse Free Sources under diverse feeling. At that point, Granger Causal for connectivity gauges around these Iss, counting the incomplete controlled cognizance (PDC) [1] and immediate controlled exchange capacity was figured. With above Iss and effects of Granger-Causal connectivity gauges, we developed the causal connectivity mind system for distinctive passionate state. At last, in light of the Granger causality system evaluated by GCA [1], realistic trademark example of the element mind system for distinctive passionate states was quantitatively portrayed by GTA [4]. The particular ICA calculation utilized was Fastica. One of the explanations behind this decision was the extensive number of fruitful requisitions in different fields of information mining, especially therapeutic indicator preparing. The other components were the wide accessibility of the calculation and the assorted qualities in the way the unmixing grid is assessed [1].
In 2013 Mi-Sook Park, arranged three feelings by utilizing EEG indicators. Straight discriminant investigation (LDA) [2] utilizing three sorts of EEG attributes demonstrated that the mean distinguishment correctness was 66.3 [13]. These discoveries uncover that three feelings were effectively ready to be ordered taking into account EEG indicators. The EEG sign was band-pass sifted (0.7- -46) [13] and an inspecting rate was 256 Hz [13]. The EEG indicators were dissected for 30 seconds from the gauge and the passionate state. The enthusiastic states were dictated by the aftereffect of member's report toward oneself (scene that feeling is most determinedly instigated throughout presentation of every jolt). Absolute forty-eight characteristics were extricated from recurrence groups (theta, alpha, beta, and gamma), cerebral asymmetry, and EEG cognizance. Straight discriminant dissection (LDA) [13] was utilized to order three feelings (i.e., outrage, fear, and shock) [13]. These discoveries demonstrate that EEG signs were proficient to perceive outrage, fear, and shock feelings [13]. The effects may be valuable specifically circumstances where the feeling needs to be perceived with no noticeable facial and verbal interpretations [13].
M. Murugappa in 2013 worked on Emotion analysis using STFT [8]. EEG indicators were surrounded into a brief time span of 5s and two measurable characteristics in four frequency [8] groups to be specific alpha (8 Hz - 16 Hz) [8], beta (16 Hz - 32 Hz) [8], gamma (32 Hz - 60 Hz) [8] and alpha to gamma (8 Hz - 60 Hz) [8] are concentrated utilizing Fast Fourier Transform (FFT) [8]. These characteristics were mapped into the relating feelings utilizing two basic classifiers, for example, K Nearest Neighbor (knn) [8] and Probabilistic Neural Network (PNN) [8]. This present work is mean to investigate the brief time EEG indicators for feeling grouping utilizing Fast Fourier Change (FFT) [8]. The proposed two measurable characteristics perform better on arranging the feelings utilizing two straightforward classifiers (KNN and PNN) [8]. Be that as it may, KNN performs well over PNN [8] with lesser computational multifaceted nature and giving the most extreme mean feeling arrangement on ordering five feelings. They additionally broke down the EEG [4] motions over distinctive recurrence groups for feeling order. Beta recurrence band give more better data's about the enthusiastic state progressions of the subjects over other recurrence groups. Likewise, these effects additionally affirm our theory that it is conceivable to separate and characterize the human feelings the direct and non-straight characteristics. In this work, KNN beats PNN by offering the most extreme mean grouping exactness of 91.33 % [8] on beta band. This test outcomes demonstrates the brief time span of EEG signals is profoundly fundamental for identifying the enthusiastic state progressions of the subjects [8].
In [14] researchers studied about EEG signal [14] examination system, Arnaud Delorme endeavored to manage power at 12 Hz over the left- and right-focal scalp territory to control the elevation of a cursor moving to target boxes set at the top, centre, or lowest part-right of a workstation screen [14]. We measured EEG indicators from 0 Hz to 50 Hz; these were converted into force spectra utilizing FFTs [14]. We measured delta (0~4 Hz) [14], theta (4~8 Hz) [14], alpha (8~13 Hz) [14], beta (13~30 Hz) [14], and gamma (30~50 Hz) [14]. The figured relative force qualities were thought about with the database for feeling distinguishment. The ensuing values plainly contrasted by single person. We assessed feelings utilizing likelihood deduction within request to record for the contrasts. When we contrasted the outcomes and the database, the mean qualities of the database were the benchmarks of the worth judgements.
M. Islam explored to assess the distinctive human feelings through Electroencephalogram (EEG) signal [16] and to accept data about the inner progressions of cerebrum state. The paper exhibits the location of human feeling focused around some striking characteristics of EEG signal [16]. For this reason, seven enthusiastic states have been specified, for example, unwind, thought, memory related, engine movement, average, fear, and getting a charge out of music. A few EEG signals [16] have been gathered for these states and investigated utilizing recurrence change and factual measures. Diverse noteworthy characteristics have been concentrated from the examined signal [16]. Around different factual measures skewness and kurtosis are picked which demonstrate the biggest scattering in diverse mental states and help to assess distinctive human feelings [16]. Recurrence investigation demonstrates how the extents of greatness differ with diverse recurrence parts. On the premise of extent extents distinctive enthusiastic states are recognized. EEG signal [16] gives a powerful path in the working of the mind to investigation of mental condition. The EEG signals [16] are ordinarily pre-processed to distinguish the antiquities that may be available in the signal [16]. Regularly EEG [16] ages defiled with antiques are rejected. Indeed with antique dismissal, EEG signals [16] are influenced by commotion which is then evacuated utilizing versatile separating. The fundamental methodology to sign examination is to get fitting data from the indicator by applying the best suitable strategy. The characteristics extricated from the dissected indicator are acknowledged as the perfect attributes of the singular passionate states which are contrasted and some testing sign to distinguish the obscure states. The routines utilized within this study are Fourier and Statistical examination [16]. This work proposed the improvement of human feeling distinguishment in diverse ecological conditions through EEG signal [16]. For this reason, a few EEG information [16] sets in distinctive ecological states were gotten and broke down utilizing factual and recurrence change. After examination, some striking characteristics were concentrated for feeling distinguishment [16]. From these characteristics, the huge deviations around the prespecified states were watched for a few subjects who help to locate the mental states. The skewness of EEG signal decided the peakedness in the state of unwind, thought, memory, engine movement, dread, charming state and appreciating music [16].
In [17] details were provided regarding a novel information procurement framework, as a feature of a task to measure reaction- coupled ERP [17] indicators, which utilized wavelet conversion to decay EEG motions [17] in genuine time in their separate vitality groups. Because of stipulations in DSP-based [17] figuring force, they had to settle for the short of what ideal disintegration by short wavelets [17], however by and by attain tasteful segregation power for clinical requisitions. In spite of our advancement in executing quick wavelet change (WT) [17] calculations, we need to give careful consideration to the constrained processing power and indicator postponements and consequently can't utilize high-request wavelets like Daubechie's No 8 (db8) [17], yet rather need to settle for more modest ones like Daubechie's No 3 (db3) [17].
In 2007 representation on every emotion, three emotions have been related to higher assention [18]. In the wake of preprocessing the signs, discrete wavelet change is utilized to concentrate the EEG parameters. The characteristic vectors determined from the above characteristic extraction system on 63 [18] biosensors structure a data lattice for feeling grouping. In this work, we have utilized Fuzzy C-Means (FCM) [18] and Fuzzy k-Means (FKM) [18] grouping techniques for characterizing the feelings. We have likewise investigated the execution of FCM [18] and FKM [18] on diminished number of 24 biosensors model. At last, we looked at the execution of grouping the discrete feelings utilizing FCM [18] and FKM [18] on both 64 [18] biosensors and 24 [18] biosensors. Effects affirm the likelihood of utilizing wavelet change based characteristic extraction for surveying the human feelings from EEG signal, and of selecting an insignificant number of channels for feeling distinguishment test [18].

Figure 2 5: Channel electrode placement for EEG
The recording of EEG signal [18] ordinarily tainted with foundation clamors. Then again, the wavelet change differentiates the high frequency [18] parts (clamors) from the first EEG signal [18]. There may be a chance for a minor measure of clamors that exist in the first sign. Thusly, entropy could be utilized for measuring the helpful data about the EEG indicator for feeling from the meddlesome commotion.

Figure 2 6: Sub band of EEG Signal
We quickly reported the multi-determination dissection of wavelet capacity based characteristic extraction of feeling distinguishment utilizing EEG signals. Information base has been made for both 63 and 24 [18] channels EEG information for three feelings utilizing vitality and entropy characteristics.
In [19] they have broken down the EEGs utilizing different trademark measures like Correlation Dimension, Lya-punov type, Hurst Exponent, and Approximate Entropy. The decision of an appropriate consumed deferral is ascertained utilizing the insignificant common information system [19]. Association Dimension is a standout amongst the most broadly utilized measures of Fractal Dimension [19]. The exactness of the nonlinear time arrangement investigation lies in the choice of ideal implanting measurement [19].for functional provisions, it is best to apply the Grassberger [19] and Pro-caccia calculation [19] and figure the Dcorr for different inserting measurement. At that point the base installing measurement of the attractor for coordinated inserting is m+1, where m is the implanting measurement above which the Dcorr soaks. In the estimation Apen, two parameters m and r must be picked before the processing of Apen where, m indicates the example length, and r is the commotion edge. For this study, m is situated to 2 and r is situated to 15% of the standard deviation [19] of each one time arrangement. 'Lyapunov Exponent (??)' [19] is a quantitative measure of the delicate reliance on the starting conditions.it is a measure of the rate at which the trajectories separate one from other.it is a measure of the rate at which the trajectories separate one from other. The indicator gets less perplexing (less arbitrary) when the individual is subjected music (of his decision) or under reflex logical [19] incitement. Additionally Largest Lyapunov [19] Exponent, Approximate Entropy and Hurst Exponent are computed for all the information sets. Biggest Lyapunov [19] Exponent acts as marker of long haul-conduct. The LLE [19] esteem closer to 1 demonstrates the confused conduct of the arrangement. This quality falls because of the impact of the music and reflex logical [19] incitement.

3
4 REFERENCE
[1]. CHEN Dongwei, et al., 'EEG-based Emotion recognition with Brain Network using Independent Components Analysis and Granger Causality', 2013 IEEE.
[2]. Mandeep Singh, Mooninder Singh, et.al., 'Feature Extraction from EEG for Emotion Classification', Volume 7 ' Number 1 ' IEEE,December 2013 pp. 6-1
[3]. Saima Aman, 'Recognizing Emotions in Text', Canada, 2007
[4]. John R. Williamsand, Kevin Amaratungay, 'Introduction to Wavelets in Engineering', Intelligent Engineering Systems Laboratory, Massachusetts Institute of Technology, MA 02139,USA
[5]. M. Sifuzzaman, M.R. Islam and M.Z. Ali, 'Application of Wavelet Transform and its Advantages Compared to Fourier Transform', Journal of Physical Sciences, Vol. 13, 2009, 121-134
[6]. Jun Yao, et al., 'Bionic Wavelet Transform: A New Time'Frequency Method Based on an Auditory Model', IEEE transactions on biomedical engineering, vol. 48, no. 8, august 2001
[7]. Omid Sayadi and Mohammad B. Shamsollahi, 'Multiadaptive Bionic Wavelet Transform: Application to ECG Denoising and Baseline Wandering Reduction' , EURASIP Journal on Advances in Signal Processing Volume 2007, Article ID 41274, 11 pages doi:10.1155/2007/41274
[8]. M Murugappan, et al., 'Human Emotion Recognition Through Short Time Electroencephalogram (EEG) Signals Using Fast Fourier Transform (FFT)', 2013 IEEE 9th International Colloquium on Signal Processing and its Applications, 8 - 10 Mac. 2013, Kuala Lumpur, Malaysia
[9]. Dr. Mandeep Singh, et al., 'ANN based emotion recognition along valence axis using EEG', Volume 7 ' Number 1 ' , IEEE, December 2013 pp. 56-60.
[10]. Andrew J. Niemiec, Brian J. Lithgow, 'AIM Alpha-band characteristics in EEG spectrum indicate reliability of frontal brain asymmetry measures in diagnosis of depression', Engineering in Medicine and Biology 27th Annual Conference Shanghai, China, September 1-4, 2005.
[11]. Tanu Sharma, et al., 'Emotion Estimation using Physiological Signals', IEEE
[12]. Rendi E. J. Yohanes, 'Discrete Wavelet Transform Coefficients for Emotion Recognition from EEG Signals', 34th Annual International Conference of the IEEE EMBS San Diego, California USA, 28 August - 1 September, 2012.
[13]. Teodiano Freire Bastos-Filho, 'Evaluation of Feature Extraction Techniques in Emotional State Recognition', IEEE Proceedings of 4th International Conference on Intelligent Human Computer Interaction, Kharagpur, India, December 27-29, 2012.
[14]. Park, M., Dh, H., Jeong, H., & Sohn, J, 'EEG-based emotion recogntion during emotionally evocative films', 2013 pp no.56'57.
[15]. Dongwei, C., Fang, W., Zhen, W., Haifang, L., & Junjie, C., 'EEG-based emotion recognition with brain network using independent components analysis and granger causality', 2013 International Conference on Computer Medical Applications (ICCMA), 1'6. doi:10.1109/ICCMA.2013.6506157
[16]. Islam, M., Ahmed, T., Mostafa, S. S., Yusuf, M. S. U., & Ahmad, M., 'Human emotion recognition using frequency & statistical measures of EEG signal', 2013 International Conference on Informatics, Electronics and Vision (ICIEV), 1'6. doi:10.1109/ICIEV.2013.6572658
[17]. Malina, T., Folkers, A., Hofmann, U. G., & Processing, S. 'Real-time EEG processing based on Wavelet Transformation', June, 2002.
[18]. Murugappan, M., Rizon, M., Nagarajan, R., Yaacob, S., Zunaidi, I., & Hazry, D., 'EEG feature extraction for classifying emotions using FCM and FKM', International Journal of Computers and Communications,2001, 1, 21'25.
[19]. Natarajan, K., Acharya U, R., Alias, F., Tiboleng, T., & Puthusserypady, S. K., 'Nonlinear analysis of EEG signals at different mental states. Biomedical Engineering Online',(2004),3,7. doi:10.1186/1475-925X-3-7
[20]. Levkov, C., Mihov, G., Ivanov, R., Daskalov, I., Christov, I., & Dotsinsky, I. (2005). Removal of power-line interference from the ECG: a review of the subtraction procedure. Biomedical Engineering Online, 4, 50. doi:10.1186/1475-925X-4-50
[21]. Nawrocka, A., & Kot, A. ,'Methods For EEG Signal Analysis', (2011), 266'269.
[22]. Naderi, M. A., 'Analysis and classification of EEG signals using spectral analysis and recurrent neural networks', (November) (2010), 3'4.
[23]. Shen, M., Chang, G., & Wang, S. (2006). EEG Signal Analysis Based on Time-variant Coupled Network Lattice Model, 123'127.
[24]. Sun, L., Liu, Y., & Issunstueducn, E. (2005). Independent Component Analysis of EEG Signals, 0'3.
[25]. Yong, L., & Shengxun, Z. (1996). Apply Wavelet Transform To Analyse Eeg Signal, 1007'1008.
[26]. Mylonas, S. A., & Comley, R. A. (1994). EEG Signal Analysis Using a Multi-Layer Perceptron with Linear Preprocessing, 671'680.
[27]. Levkov, C., Mihov, G., Ivanov, R., Daskalov, I., Christov, I., & Dotsinsky, I. (2005). Removal of power-line interference from the ECG: a review of the subtraction procedure. Biomedical Engineering Online, 4, 50. doi:10.1186/1475-925X-4-50
[28]. Kaminaka, J., Yamaguchi, T., Taniguchi, M., Ohmori, K., Watanabe, S., Inoue, K., & Pfurtscheller, G. (2008). EEG Signal Analysis during Miss Operation in BCI System, 3184'3188.
[29]. Pfurtscheller, G., & Neuper, C. (2001). Motor Imagery and Direct Brain ' Computer Communication, 89(7), 1123'1134.
[30]. Schl??gl, A., Obermaier, B., & Pregenzer, M. (2000). Current Trends in Graz Brain ' Computer Interface ( BCI ), 8(2), 216'219.
[31]. Easwaramoorthy, D., & Uthayakumar, R. (2010). Analysis of EEG Signals using Advanced Generalized Fractal Dimensions.
[32]. Hema, C. R., Paulraj, M. P., Yaacob, S., Adom, A. H., & Nagarajan, R. (2009). EEG Motor Imagery Classification of Hand Movements for a Brain Machine Interface, 14(2), 49'56.
[33]. Abootalebi, V., Hassan, M., & Ali, M. (2008). A new approach for EEG feature extraction in P300-based lie, 4, 48'57. doi:10.1016/j.cmpb.2008.10.001
[34]. Zabidi, A., Mansor, W., Lee, K. Y., Fadzal, C. W. N. F. C. W., Mara, U. T., Alam, S., & Acquisitions, A. D. (2012). Average Spectral Analysis of EEG Signal From Imagined Writing, (December), 960'963.
[35]. Kumar, B. S. (n.d.). EEG SIGNALS ANALYSIS SWISS FEDERAL INSTITUTE OF TECHNOLOGY ,.
[36]. Abdulhamit Subasi ,(2005). NEURAL NETWORK CLASSIFICATION OF EEG SIGNALS BY USING AR, 10(1), 57'70.
[37]. Cowie R., Douglas-Cowie E., Tsapatsoulis N., Votsis G., Kollias S., Fellenz W. and Taylor J., "Emotional recognition in human computer interaction", IEEE Signal Processing Magazine, pp. 32-80, 2001.
[38]. Devillers L., Vasilescu I. and Lamel L., "Challenges in real-life emotion annotation and machine learning based detection", Neural Networks, Elsevier, pp. 407-422, 2005.
[39]. Khosrowabadi R., Wahab A., Ang K.K. and Baniasad M.H., "Affective computation on EEG correlates of emotion from musical and vocal stimuli", Int. Joint Conference on Neural Networks, Atlanta, Georgia, 2009.
[40]. Khalili Z., Moradi M.H., "Emotion Recognition System Using Brian and Peripheral Signals Using Correlation Dimension to Improve the Results of EEG", Int. Joint Conference on Neural Networks, Atlanta, Georgia, 2009.
[41]. Lin Y.P., Wang C.H., Wu T.L., Jeng S.K. and Chen J.H., "EEG-based emotion recognition in music listening: A comparison of schemes for multiclass support vector machine", in Proc. of IEEE ICASSP, pp. 489-492, 2009.
[42]. J. Zhou, C. Yu, J. Riekki and E. Ka??rkka??inen, "AmE Framework: a Model for Emotion-aware Ambient Intelligence, 2007," The Second International Conference on Affective Computing and Intelligent Interaction (ACII2007): Lisbon, Portugal., 2007.

[43]. M. Tkal&ccaron,i&ccaron,, U. Burnik and A. Ko&scaron,ir, &ldquo,Using Affective Parameters in a Content-Based Recommender System for Images,&rdquo, User Modeling and User-Adapted Interaction, vol. 20, pp. 1-33-33, Sept. 2010.
[44]. M. Pantic, M. Valstar, R. Rademaker and L. Maat, &ldquo,Web-Based Database for Facial Expression Analysis,&rdquo, Proc. Int',l Conf. Multimedia and Expo, pp. 317-321, 2005.
[45]. G. Fanelli, J. Gall, H. Romsdorfer, T. Weise and L. Van Gool, &ldquo,A 3-D Audio-Visual Corpus of Affective Communication,&rdquo, IEEE Trans. Multimedia, vol. 12, no. 6, pp. 591-598, Oct. 2010.
[46]. J.A. Healey, &ldquo,Wearable and Automotive Systems for Affect Recognition from Physiology,&rdquo, PhD dissertation, MIT, 2000.

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